Flowchart

library(GENIE3)
library(doParallel)
library(igraph)
library(tidyverse)
library(DT)
library(reticulate)
library(learn2count)
library(rbenchmark)
library(reshape2)
library(gridExtra)
library(DiagrammeR)
library(pROC)
library(JRF)
library(DiagrammeRsvg)
library(rsvg)
library(RColorBrewer)
library(rbenchmark)
library(ZILGM)
library(patchwork)

use_python("/usr/bin/python3", required = TRUE)
arboreto <- import("arboreto.algo")
pandas <- import("pandas")
numpy <- import("numpy")

execution_times <- list()
source("generate_adjacency.R")
source("symmetrize.R")
source("pscores.R")
source("plotg.R")
source("compare_consensus.R")
source("create_consensus.R")
source("earlyj.R")
source("plotROC.R")
source("cutoff_adjacency.R")
source("infer_networks.R")
grViz_output <- DiagrammeR::grViz("
digraph biological_workflow {
  # Set up the graph attributes
  graph [layout = dot, rankdir = TB]

  # Define consistent node styles
  node [shape = rectangle, style = filled, color = lightblue, fontsize = 12]

  # Define nodes for each step
  StartNode [label = 'Ground Thruth - String Regulatory Network', shape = oval, color = seagreen, fontcolor = black]
  AdjacencyMatrix [label = 'Thruth Adjacency Matrix', shape = rectangle, color = seagreen]
  SimulateData [label = 'Simulate Single-Cell Data', shape = rectangle, color = goldenrod]

  # Reconstruction using Three Packages
  LateIntegration [label = 'Late\nIntegration', shape = oval, color = khaki]
  EarlyIntegration [label = 'Early\nIntegration', shape = oval, color = khaki]
  Jointanalysis [label = 'Joint\nanalysis', shape = oval, color = khaki]
  

  # Process 
  earlyj [label = 'earlyj.R', shape=diamond, color=lightblue, fontcolor=black]
  networkinference [label = 'infer_networks.R\nGENIE3\nGRNBoost2\nZILGM\nJRF', shape = rectangle, color = goldenrod, fontcolor=black]
  symmetrize [label = 'symmetrize.R', shape = rectangle, color = goldenrod, fontcolor=black]
  plotROC [label = 'plotROC.R', shape=diamond, color=lightblue, fontcolor=black]
  generateadjacency [label='generate_adjacency.R\nWeighted Adjacency', shape=rectangle, color=goldenrod, fontcolor=black]
  cutoffadjacency [label='cutoff_adjacency.R\nBinary Adjacency', shape=rectangle, color=goldenrod, fontcolor=black]
  pscores [label='pscores.R\nTPR\nFPR\nF1\nAccuracy\nPrecision', shape=diamond, color=lightblue, fontcolor=black]
  voting [label='Voting\nUnion\nIntersection', shape=diamond, color=lightblue, fontcolor=black]
  plotgcompare  [label='plotg.R\ncompare_consesus.R\nPlot Graphs', shape=rectangle, color=goldenrod, fontcolor=black]

  # Define the workflow structure
  StartNode -> AdjacencyMatrix
  AdjacencyMatrix -> SimulateData
  SimulateData -> LateIntegration
  SimulateData -> EarlyIntegration
  SimulateData -> Jointanalysis
  EarlyIntegration -> earlyj
  earlyj -> networkinference
  LateIntegration -> networkinference
  Jointanalysis -> networkinference
  networkinference -> symmetrize
  symmetrize -> plotROC
  symmetrize -> generateadjacency
  generateadjacency -> cutoffadjacency
  cutoffadjacency -> pscores
  cutoffadjacency -> voting
  voting -> plotgcompare
  voting -> pscores
}
")

svg_code <- export_svg(grViz_output)
rsvg::rsvg_png(charToRaw(svg_code), "./../analysis/flowchart.png")

grViz_output

Tcell Ground Truth

adjm <- read.table("./../data/adjacency_matrix.csv", header = T, row.names = 1, sep = ",") %>% as.matrix()
diag(adjm) <- 0

adjm %>%
    datatable(extensions = 'Buttons',
            options = list(
              dom = 'Bfrtip',
              buttons = c('csv', 'excel'),
              scrollX = TRUE,
              pageLength = 10), 
            caption = "Ground Truth")
gtruth <- igraph::graph_from_adjacency_matrix(adjm, mode = "undirected", diag = F)

num_nodes <- vcount(gtruth)
num_edges <- ecount(gtruth)

set.seed(1234)
plot(gtruth, 
     main = paste("Ground Truth\nNodes:", num_nodes, "Edges:", num_edges),
     vertex.label.color = "black",
     vertex.size = 6, 
     edge.width = 2, 
     vertex.label = NA,
     vertex.color = "steelblue",
     layout = igraph::layout_with_fr)

Simulate Data

ncell <- 500
nodes <- nrow(adjm)

set.seed(1130)
mu_values <- c(3, 6, 9)

count_matrices <- lapply(1:3, function(i) {
  set.seed(1130 + i)
  mu_i <- mu_values[i]
  
  count_matrix_i <- simdata(n = ncell, p = nodes, B = adjm, family = "ZINB", 
                            mu = mu_i, mu_noise = 1, theta = 0.5, pi = 0.2)
  
  count_matrix_df <- as.data.frame(count_matrix_i)
  colnames(count_matrix_df) <- colnames(adjm)
  rownames(count_matrix_df) <- paste("cell", 1:nrow(count_matrix_df), sep = "")
  
  return(count_matrix_df)
})

count_matrices[[1]] %>%
    datatable(extensions = 'Buttons',
            options = list(
              dom = 'Bfrtip',
              buttons = c('csv', 'excel'),
              scrollX = TRUE,
              pageLength = 10), 
            caption = "Simulated count matrix")
saveRDS(count_matrices, "./../analysis/count_matrices.RDS")

Matrices Integration

Late Integration

GENIE3

set.seed(1234)
tictoc::tic("GENIE3 late")
genie3_late <- infer_networks(count_matrices, method="GENIE3")
saveRDS(genie3_late, "./../analysis/genie3_late.RDS")
execution_times[['GENIE3 late']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GENIE3 late: 132.907 sec elapsed
genie3_late[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GENIE3 output")

symmetrize Output and ROC

source("plotROC.R")
genie3_late_wadj <- generate_adjacency(genie3_late, ground.truth = adjm)
sgenie3_late_wadj <- symmetrize(genie3_late_wadj, weight_function = "mean")
plotROC(sgenie3_late_wadj, adjm, plot_title = "ROC curve - GENIE3 Late Integration")

sgenie3_late_wadj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GENIE3 symmetrize output")

Generate Adjacency and Apply Cutoff

sgenie3_late_adj <- cutoff_adjacency(count_matrices = count_matrices,
                 weighted_adjm_list = sgenie3_late_wadj, 
                 ground.truth = adjm,
                 n = 2,
                 method = "GENIE3")
## Matrix 1 Mean 95th Percentile Cutoff: 0.009957001 
## Matrix 2 Mean 95th Percentile Cutoff: 0.00990153 
## Matrix 3 Mean 95th Percentile Cutoff: 0.00968373
sgenie3_late_adj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GENIE3 adjacency")

Comparison with the Ground Truth

scores.genie3.late.all <- pscores(adjm, sgenie3_late_adj)

scores.genie3.late.all$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
plots <- plotg(sgenie3_late_adj)

consesusm <- create_consensus(sgenie3_late_adj, method="vote")
consesusu <- create_consensus(sgenie3_late_adj, method="union")

scores.genie3.late <- pscores(adjm, list(consesusm))

scoresu.genie3.late <- pscores(adjm, list(consesusu))

scores.genie3.late$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
scores.genie3.late$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
ajm_compared <- compare_consensus(consesusm, adjm)

ajm_compared <- compare_consensus(consesusu, adjm)

GRNBoost2

set.seed(1234)
tictoc::tic("GRNBoost2 late")
grnb_late <- infer_networks(count_matrices, method="GRNBoost2")
saveRDS(grnb_late, "./../analysis/grnb_late.RDS")
execution_times[['GRNBoost2 late']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GRNBoost2 late: 9.241 sec elapsed
grnb_late[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GRNBoost2 output")

symmetrize Output and ROC

grnb_late_wadj <- generate_adjacency(grnb_late, ground.truth = adjm)
sgrnb_late_wadj <- symmetrize(grnb_late_wadj, weight_function = "mean")
plotROC(sgrnb_late_wadj, adjm, plot_title = "ROC curve - GRNBoost2 Late Integration")

sgrnb_late_wadj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GRNBoost2 symmetrize output")

Generate Adjacency and Apply Cutoff

sgrnb_late_adj <- cutoff_adjacency(count_matrices = count_matrices,
                 weighted_adjm_list = sgrnb_late_wadj, 
                 ground.truth = adjm,
                 n = 2,
                 method = "GRNBoost2")
## Matrix 1 Mean 95th Percentile Cutoff: 0.8679026 
## Matrix 2 Mean 95th Percentile Cutoff: 0.8586811 
## Matrix 3 Mean 95th Percentile Cutoff: 0.8864974
sgrnb_late_adj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GRNBoost2 adjacency")

Comparison with the Ground Truth

scores.grn.late.all <- pscores(adjm, sgrnb_late_adj)

scores.grn.late.all$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
plots <- plotg(sgrnb_late_adj)

consesusm <- create_consensus(sgrnb_late_adj, method="vote")
consesusu <- create_consensus(sgrnb_late_adj, method="union")

scores.grn.late <- pscores(adjm, list(consesusm))

scoresu.grn.late <- pscores(adjm, list(consesusu))

scores.grn.late$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
scoresu.grn.late$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
ajm_compared <- compare_consensus(consesusm, adjm)

ajm_compared <- compare_consensus(consesusu, adjm)

ZILGM Park

set.seed(1234)
tictoc::tic("ZILGM late")
zilgm_late <- infer_networks(count_matrices_list = count_matrices, method = "ZILGM", adjm = adjm)
## learning for NBII graphical model 
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress :  10 % 
Conducting sampling in progress :  20 % 
Conducting sampling in progress :  30 % 
Conducting sampling in progress :  40 % 
Conducting sampling in progress :  50 % 
Conducting sampling in progress :  60 % 
Conducting sampling in progress :  70 % 
Conducting sampling in progress :  80 % 
Conducting sampling in progress :  90 % 
Conducting sampling in progress :  100 % 
learning for NBII graphical model 
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress :  10 % 
Conducting sampling in progress :  20 % 
Conducting sampling in progress :  30 % 
Conducting sampling in progress :  40 % 
Conducting sampling in progress :  50 % 
Conducting sampling in progress :  60 % 
Conducting sampling in progress :  70 % 
Conducting sampling in progress :  80 % 
Conducting sampling in progress :  90 % 
Conducting sampling in progress :  100 % 
learning for NBII graphical model 
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress :  10 % 
Conducting sampling in progress :  20 % 
Conducting sampling in progress :  30 % 
Conducting sampling in progress :  40 % 
Conducting sampling in progress :  50 % 
Conducting sampling in progress :  60 % 
Conducting sampling in progress :  70 % 
Conducting sampling in progress :  80 % 
Conducting sampling in progress :  90 % 
Conducting sampling in progress :  100 % 
Assigned lamb to lambda_results[[ 1 ]] with 50 matrices.
## Assigned lamb to lambda_results[[ 2 ]] with 50 matrices.
## Assigned lamb to lambda_results[[ 3 ]] with 50 matrices.
saveRDS(zilgm_late, "./../analysis/zilgm_late.RDS")
execution_times[['ZILGM late']] <- tictoc::toc(log = TRUE)$toc[[1]]
## ZILGM late: 3978.19 sec elapsed
est_graphs <- zilgm_late$network_results
lambdas <- zilgm_late$lambda_results
scores.zilgm1 <- pscores(adjm, lambdas[[1]])
scores.zilgm2 <- pscores(adjm, lambdas[[2]])
scores.zilgm3 <- pscores(adjm, lambdas[[3]])
m1 <- scores.zilgm1$Statistics %>% mutate(K="M1") 
m2 <- scores.zilgm2$Statistics %>% mutate(K="M2") 
m3 <- scores.zilgm3$Statistics %>% mutate(K="M3") 

allm <- rbind(m1,m2,m3)
ggplot(allm, aes(x=FPR, y=TPR, color=K)) +
  geom_point() +
  geom_line(size=1.2) +
  labs(x="False Positive Rate (1 - Specificity)",
         y="True Positive Rate (Sensitivity)") +
  theme_minimal() +
  theme(legend.position = "bottom") 

consensus_matrices <- vector("list", 50)

for (i in 1:50) {
  ranklambda <- list(lambdas[[1]][[i]], lambdas[[2]][[i]], lambdas[[3]][[i]])
  consensus_matrices[[i]] <- create_consensus(ranklambda, method="vote")
}

scores.zilgm.voting <- pscores(adjm, consensus_matrices)
scores.zilgm.voting$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
ggplot(scores.zilgm.voting$Statistics, aes(x=FPR, y=TPR)) +
  geom_point() +
  geom_line(size=1.2) +
  labs(x="False Positive Rate (1 - Specificity)",
         y="True Positive Rate (Sensitivity)") +
  theme_minimal() +
  theme(legend.position = "bottom") 

Comparison with the Ground Truth

scores.zilgm.late.all <- pscores(adjm, est_graphs)

scoresu.zilgm.late.all <- pscores(adjm, est_graphs)

scores.zilgm.late.all$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
scoresu.zilgm.late.all$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
plots <- plotg(est_graphs)

consesusm <- create_consensus(est_graphs, method="vote")
consesusu <- create_consensus(est_graphs, method="union")

scores.zilgm.late <- pscores(adjm, list(consesusm))

scoresu.zilgm.late <- pscores(adjm, list(consesusu))

scores.zilgm.late$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
scoresu.zilgm.late$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
ajm_compared <- compare_consensus(consesusm, adjm)

ajm_compared <- compare_consensus(consesusu, adjm)

Early Integration

early_matrix <- list(earlyj(count_matrices))

GENIE3

set.seed(1234)
tictoc::tic("GENIE3 early")
genie3_early <- infer_networks(early_matrix, method="GENIE3")
execution_times[['GENIE3 early']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GENIE3 early: 152.035 sec elapsed
saveRDS(genie3_early, "./../analysis/genie3_early.RDS")

genie3_early[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GENIE3 output")

symmetrize Output and ROC

genie3_early_wadj <- generate_adjacency(genie3_early, ground.truth = adjm)
sgenie3_early_wadj <- symmetrize(genie3_early_wadj, weight_function = "mean")
plotROC(sgenie3_early_wadj, adjm, plot_title = "ROC curve - GENIE3 Early Integration")

sgenie3_early_wadj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GENIE3 symmetrize output")

Generate Adjacency and Apply Cutoff

sgenie3_early_adj <- cutoff_adjacency(count_matrices = early_matrix,
                 weighted_adjm_list = sgenie3_early_wadj, 
                 ground.truth = adjm,
                 n = 2,
                 method = "GENIE3")
## Matrix 1 Mean 95th Percentile Cutoff: 0.01013741
sgenie3_early_adj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GENIE3 adjacency")

Comparison with the Ground Truth

scores.genie3.early <- pscores(adjm, sgenie3_early_adj)

scores.genie3.early$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
plots <- plotg(sgenie3_early_adj)

ajm_compared <- compare_consensus(sgenie3_early_adj[[1]], adjm)

GRNBoost2

set.seed(1234)
tictoc::tic("GRNBoost2 early")
grnb_early <- infer_networks(early_matrix, method="GRNBoost2")
execution_times[['GRNBoost2 early']] <- tictoc::toc(log = TRUE)$toc[[1]]
## GRNBoost2 early: 11.234 sec elapsed
saveRDS(grnb_early, "./../analysis/grnb_early.RDS")

grnb_early[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GRNBoost2 output")

symmetrize Output and ROC

grnb_early_wadj <- generate_adjacency(grnb_early, ground.truth = adjm)
sgrnb_early_wadj <- symmetrize(grnb_early_wadj, weight_function = "mean")
plotROC(sgrnb_early_wadj, adjm, plot_title = "ROC curve - GRNBoost2 Early Integration")

grnb_early_wadj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GRNBoost2 symmetrize output")

Generate Adjacency and Apply Cutoff

sgrnb_early_adj <- cutoff_adjacency(count_matrices = early_matrix,
                 weighted_adjm_list = sgrnb_early_wadj, 
                 ground.truth = adjm,
                 n = 2,
                 method = "GRNBoost2")
## Matrix 1 Mean 95th Percentile Cutoff: 4.281737
sgrnb_early_adj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "GRNBoost2 adjacency")

Comparison with the Ground Truth

scores.grn.early <- pscores(adjm, sgrnb_early_adj)

scores.grn.early$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
plots <- plotg(sgrnb_early_adj)

ajm_compared <- compare_consensus(sgrnb_early_adj[[1]], adjm)

ZILGM Park

set.seed(1234)
tictoc::tic("ZILGM early")
zilgm_late <- infer_networks(count_matrices_list = early_matrix, method = "ZILGM", adjm = adjm)
## learning for NBII graphical model 
## nlambda : 50
## penalty function : LASSO
## update type : IRLS
## Conducting sampling in progress :  10 % 
Conducting sampling in progress :  20 % 
Conducting sampling in progress :  30 % 
Conducting sampling in progress :  40 % 
Conducting sampling in progress :  50 % 
Conducting sampling in progress :  60 % 
Conducting sampling in progress :  70 % 
Conducting sampling in progress :  80 % 
Conducting sampling in progress :  90 % 
Conducting sampling in progress :  100 % 
Assigned lamb to lambda_results[[ 1 ]] with 50 matrices.
saveRDS(zilgm_late, "./../analysis/zilgm_early.RDS")
execution_times[['ZILGM early']] <- tictoc::toc(log = TRUE)$toc[[1]]
## ZILGM early: 3605.811 sec elapsed
est_graphs <- zilgm_late$network_results
lambdas <- zilgm_late$lambda_results
scores.zilgm1 <- pscores(adjm, lambdas[[1]])
m1 <- scores.zilgm1$Statistics %>% mutate(K="M1") 
ggplot(m1, aes(x=FPR, y=TPR, color=K)) +
  geom_point() +
  geom_line(size=1.2) +
  labs(x="False Positive Rate (1 - Specificity)",
         y="True Positive Rate (Sensitivity)") +
  theme_minimal() +
  theme(legend.position = "bottom") 

Comparison with the Ground Truth

scores.zilgm.early <- pscores(adjm, est_graphs)

scores.zilgm.early$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
ajm_compared <- compare_consensus(est_graphs[[1]], adjm)

Joint Integration

Joint Random Forest

#https://cran.r-project.org/src/contrib/Archive/JRF/
#install.packages("/home/francescoc/Downloads/JRF_0.1-4.tar.gz", repos = NULL, type = "source")
#jrf_mat <- infer_networks(count_matrices, method="JRF")

jrf_matrices <- lapply(count_matrices, t)
jrf_matrices_norm <- lapply(jrf_matrices,function(x) {
  (x - mean(x)) / sd(x)
  })

genes <- rownames(jrf_matrices_norm[[1]])

set.seed(1234)
tictoc::tic("JRF_out")
netout <- JRF(X = jrf_matrices_norm, 
              genes.name = genes, 
              ntree = 500, 
              mtry = round(sqrt(length(genes) - 1)))
execution_times[['JRF_out']] <- tictoc::toc(log = TRUE)$toc[[1]]
## JRF_out: 1213.373 sec elapsed
netout %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "JRF output")
out.perm <- Run_permutation(jrf_matrices_norm,mtry=round(sqrt(length(genes)-1)),ntree=500, genes,3)
#out <- JRF_permutation(jrf_matrices_norm,mtry=round(sqrt(length(genes)-1)),ntree=500,genes,2)
#final.net <- JRF_network(netout,out.perm,0.001)
#final.net

Finalnet

myJRF_network <- function(out.jrf, out.perm, TH) {
  
  nclasses <- dim(out.perm)[3]
  M <- dim(out.perm)[2]
  out <- vector("list", nclasses)  

  for (net in 1:nclasses) { 
    j.np <- sort(out.jrf[, 2 + net], decreasing = TRUE)
    FDR <- matrix(0, dim(out.perm)[1], 1)
    
    th <- NULL  
    for (s in 1:length(j.np)) { 
      FP <- sum(sum(out.perm[, , net] >= j.np[s])) / M
      FDR[s] <- FP / s
      
      if (FDR[s] > TH) {
        th <- j.np[s]
        break
      }
    }
    
    out[[net]] <- out.jrf[out.jrf[, 2 + net] > th, seq(1, 2)]
  }
  
  return(out)
}

mynet <- myJRF_network(netout,out.perm,0.05)
mynet
## [[1]]
##        gene1 gene2
## 341   EEF1B2 AHNAK
## 714     RPS5 APEX1
## 2992  FCGR3A  CD14
## 3225    CD3D   CD2
## 4106   CXCR4  CD3G
## 4179   PTPRC  CD3G
## 4609    GZMK   CD6
## 7756   KLRB1  GNLY
## 8438   RACK1 IGF1R
## 10659  PTPRC  PRF1
## 10796   TFRC PTPRC
## 
## [[2]]
##          gene1    gene2
## 130     SPTBN1    ACTN1
## 797        FGR ARHGAP25
## 1043     CASP8     BCL2
## 1409       LTB    BIRC3
## 1702     PDE3B    CAMK4
## 2699      CCR8     CCR7
## 3225      CD3D      CD2
## 3229       CD5      CD2
## 3267      GZMK      CD2
## 3277      IL7R      CD2
## 3852      CD3G     CD3D
## 3858      CD8A     CD3D
## 3892      GZMK     CD3D
## 3909     KLRD1     CD3D
## 3911   LDLRAP1     CD3D
## 4012      GZMB     CD3E
## 4179     PTPRC     CD3G
## 4185      RPS5     CD3G
## 4202      TFRC     CD3G
## 4507     ITGB2     CD52
## 4838      GZMB     CD8A
## 4856     KLRB1     CD8A
## 4884     PTPRC     CD8A
## 5444     PTPRC    CXCR4
## 5881      RPS8    EIF3E
## 6903     TIGIT    FCRL3
## 7291     FOXP3    FOXP1
## 7459  TNFRSF18    FOXP3
## 8008     KLRB1     GZMB
## 8171     KLRB1     GZMK
## 8257    LILRB1   HAVCR2
## 8438     RACK1    IGF1R
## 8501      NKG7   IL10RA
## 8835     TIGIT    IL2RB
## 10230   SPTBN1    NCAM1
## 10712 SLC9A3R1    PRKCA
## 10785     SELL    PTPRC
## 11411   TNFSF8  TNFRSF8
## 
## [[3]]
## [1] gene1 gene2
## <0 rows> (or 0-length row.names)
#https://cran.r-project.org/src/contrib/Archive/JRF/
#install.packages("/home/francescoc/Downloads/JRF_0.1-4.tar.gz", repos = NULL, type = "source")
set.seed(1234)
tictoc::tic("JRF")
jrf_mat <- infer_networks(count_matrices, method="JRF")
execution_times[['JRF']] <- tictoc::toc(log = TRUE)$toc[[1]]
## JRF: 1203.537 sec elapsed
jrf_mat[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "JRF output")

Prepare the output

jrf_list <- list()

importance_columns <- grep("importance", names(jrf_mat[[1]]), value = TRUE)

for (i in seq_along(importance_columns)) {
  # Select the 'gene1', 'gene2', and the current 'importance' column
  df <- jrf_mat[[1]][, c("gene1", "gene2", importance_columns[i])]
  
  # Rename the importance column to its original name (e.g., importance1, importance2, etc.)
  names(df)[3] <- importance_columns[i]
  
  # Add the data frame to the output list
  jrf_list[[i]] <- df
}

saveRDS(jrf_list, "./../analysis/jrf.RDS")

jrf_list[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "JRF output")

symmetrize Output and ROC

jrf_wadj <- generate_adjacency(jrf_list, ground.truth = adjm)
sjrf_wadj <- symmetrize(jrf_wadj, weight_function = "mean")
plotROC(sjrf_wadj, adjm, plot_title = "ROC curve - JRF Late Integration")

sjrf_wadj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "JRF symmetrize output")

Generate Adjacency and Apply Cutoff

sjrf_adj <- cutoff_adjacency(count_matrices = count_matrices,
                 weighted_adjm_list = sjrf_wadj, 
                 ground.truth = adjm,
                 n = 2,
                 method = "JRF")
## Matrix 1 Mean 95th Percentile Cutoff: 4.940559 
## Matrix 2 Mean 95th Percentile Cutoff: 4.907554 
## Matrix 3 Mean 95th Percentile Cutoff: 4.856606
sjrf_adj[[1]] %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "JRF adjacency")

Comparison with the Ground Truth

scores.jrf.all <- pscores(adjm, sjrf_adj)

scores.jrf.all$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
plots <- plotg(sjrf_adj)

consesusm <- create_consensus(sjrf_adj, method="vote")
consesusu <- create_consensus(sjrf_adj, method="union")

scores.jrf <- pscores(adjm, list(consesusm))

scoresu.jrf <- pscores(adjm, list(consesusu))

scores.jrf$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
scoresu.jrf$Statistics %>%
    datatable(extensions = 'Buttons',
              options = list(
                dom = 'Bfrtip',
                buttons = c('csv', 'excel'),
                scrollX = TRUE,
                pageLength = 10), 
              caption = "scores")
source("compare_consensus.R")
ajm_compared <- compare_consensus(consesusm, adjm)

ajm_compared <- compare_consensus(consesusu, adjm)

#est <- PCzinb(as.matrix(count_matrices[[1]]), method="zinb1", maxcard=3, alpha=0.1)
#colnames(est) <- as.character(1:10)
#graph_est <- graph_from_adjacency_matrix(est, mode="undirected")
#plot(graph_est)

Method Comparison

mplot <- list()

for (k in c("TPR", "FPR", "Precision", "F1", "Accuracy")) {
  mplot[[k]] <- data.frame(
    GENIE3_late = scores.genie3.late$Statistics[[k]],
    GENIE3_early = scores.genie3.early$Statistics[[k]],
    GRNBoost2_late = scores.grn.late$Statistics[[k]],
    GRNBoost2_early = scores.grn.early$Statistics[[k]],
    ZILGM_late = scores.zilgm.late$Statistics[[k]],
    ZILGM_early = scores.zilgm.early$Statistics[[k]],
    JRF = scores.jrf$Statistics[[k]]
  )
}

# Convert mplot list into a long data frame for ggplot2
plot_data <- bind_rows(lapply(names(mplot), function(metric) {
  data.frame(
    Metric = metric,
    Method = names(mplot[[metric]]),
    Value = as.numeric(mplot[[metric]][1, ])
  )
}))

# Extract method groups for coloring and order the Method factor so JRF appears last
plot_data <- plot_data %>%
  mutate(Method_Group = case_when(
    grepl("GENIE3", Method) ~ "GENIE3",
    grepl("GRNBoost2", Method) ~ "GRNBoost2",
    grepl("ZILGM", Method) ~ "ZILGM",
    grepl("JRF", Method) ~ "JRF"
  )) %>%
  mutate(Method = factor(Method, levels = c(
    "GENIE3_early", "GENIE3_late", 
    "GRNBoost2_early", "GRNBoost2_late", 
    "ZILGM_early", "ZILGM_late", 
    "JRF"  # Ensure JRF is last
  )))

# Define color palette for each method group
method_colors <- c("GENIE3" = "darkblue", "GRNBoost2" = "darkgreen", "ZILGM" = "orange", "JRF" = "red")

# Create a separate ggplot for each metric, with y-axis limits set to 0-1 and no legend
plots <- lapply(unique(plot_data$Metric), function(metric) {
  # Determine if x-axis text should be displayed
  show_x_text <- metric %in% c("Accuracy", "F1")
  
  ggplot(plot_data %>% filter(Metric == metric), aes(x = Method, y = Value, fill = Method_Group)) +
    geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
    labs(title = metric, y = "Value", x = NULL) +
    scale_y_continuous(limits = c(0, 1)) +  # Set y-axis limits
    scale_fill_manual(values = method_colors) +
    theme_minimal() +
    theme(
      axis.text.x = if (show_x_text) element_text(angle = 45, hjust = 1) else element_blank(),
      axis.title.x = element_blank(),
      legend.position = "none"  # Remove legend from individual plots
    )
})

# Add time data to the plotting dataset
time_data <- data.frame(
  Method = names(execution_times),
  Time_in_Hours = unlist(execution_times) / 3600
)
time_data$Time_in_Minutes <- time_data$Time_in_Hours * 60
time_data <- time_data[order(time_data$Time_in_Hours), ]
time_data$Method <- factor(time_data$Method, levels = time_data$Method)

# Assign group colors to time_data for consistency
time_data <- time_data %>%
  mutate(Method_Group = case_when(
    grepl("GENIE3", Method) ~ "GENIE3",
    grepl("GRNBoost2", Method) ~ "GRNBoost2",
    grepl("ZILGM", Method) ~ "ZILGM",
    grepl("JRF", Method) ~ "JRF"
  ))

# Create the execution time plot
time_plot <- ggplot(time_data, aes(x = Method, y = Time_in_Hours, fill = Method_Group)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = sprintf("%.1f min", Time_in_Minutes)), vjust = -0.5) +
  labs(title = "Execution Time for Each Method", y = "Time (Hours)", x = NULL) +
  scale_fill_manual(values = method_colors) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.position = "none"
  )

# Add the execution time plot to the existing list of plots
plots <- append(plots, list(time_plot))

# Adjust the layout to accommodate the extra plot
final_plot <- (plots[[1]] | plots[[2]]) /
              (plots[[3]] | plots[[4]]) /
              (plots[[5]] | plots[[6]]) +
  plot_layout(guides = "collect") & 
  theme(legend.position = "bottom")

print(final_plot)

sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=it_IT.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=it_IT.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=it_IT.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] doRNG_1.8.2        rngtools_1.5.2     patchwork_1.1.1    ZILGM_0.1.1       
##  [5] RColorBrewer_1.1-3 rsvg_2.6.1         DiagrammeRsvg_0.1  JRF_0.1-4         
##  [9] pROC_1.18.0        DiagrammeR_1.0.11  gridExtra_2.3      reshape2_1.4.4    
## [13] rbenchmark_1.0.0   learn2count_0.3.2  reticulate_1.34.0  DT_0.22           
## [17] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.9        purrr_0.3.4       
## [21] readr_2.1.2        tidyr_1.2.0        tibble_3.1.7       ggplot2_3.3.6     
## [25] tidyverse_1.3.1    igraph_2.0.3       doParallel_1.0.17  iterators_1.0.14  
## [29] foreach_1.5.2      GENIE3_1.16.0     
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_2.0-3            ellipsis_0.3.2             
##   [3] XVector_0.34.0              GenomicRanges_1.46.1       
##   [5] fs_1.5.2                    rstudioapi_0.13            
##   [7] farver_2.1.0                fansi_1.0.3                
##   [9] lubridate_1.8.0             xml2_1.3.3                 
##  [11] splines_4.1.0               codetools_0.2-18           
##  [13] bst_0.3-24                  pscl_1.5.9                 
##  [15] knitr_1.39                  flux_0.3-0.1               
##  [17] jsonlite_1.8.0              broom_0.8.0                
##  [19] dbplyr_2.1.1                png_0.1-7                  
##  [21] graph_1.72.0                compiler_4.1.0             
##  [23] httr_1.4.3                  tictoc_1.2.1               
##  [25] backports_1.4.1             assertthat_0.2.1           
##  [27] Matrix_1.6-1.1              fastmap_1.1.0              
##  [29] cli_3.3.0                   distributions3_0.2.2       
##  [31] visNetwork_2.1.2            htmltools_0.5.2            
##  [33] tools_4.1.0                 coda_0.19-4                
##  [35] gtable_0.3.0                glue_1.6.2                 
##  [37] GenomeInfoDbData_1.2.7      V8_6.0.0                   
##  [39] Rcpp_1.0.8.3                Biobase_2.54.0             
##  [41] statnet.common_4.10.0       cellranger_1.1.0           
##  [43] jquerylib_0.1.4             vctrs_0.4.1                
##  [45] crosstalk_1.2.0             xfun_0.30                  
##  [47] network_1.18.2              rvest_1.0.2                
##  [49] lifecycle_1.0.1             MASS_7.3-57                
##  [51] zlibbioc_1.40.0             scales_1.2.0               
##  [53] hms_1.1.1                   MatrixGenerics_1.6.0       
##  [55] SummarizedExperiment_1.24.0 SingleCellExperiment_1.16.0
##  [57] yaml_2.3.5                  curl_4.3.2                 
##  [59] sass_0.4.1                  rpart_4.1.16               
##  [61] stringi_1.7.6               highr_0.9                  
##  [63] S4Vectors_0.32.4            caTools_1.18.2             
##  [65] BiocGenerics_0.40.0         shape_1.4.6                
##  [67] GenomeInfoDb_1.30.1         rlang_1.1.4                
##  [69] pkgconfig_2.0.3             matrixStats_0.62.0         
##  [71] bitops_1.0-7                evaluate_0.15              
##  [73] lattice_0.20-45             labeling_0.4.2             
##  [75] htmlwidgets_1.5.4           tidyselect_1.1.2           
##  [77] gbm_2.2.2                   plyr_1.8.7                 
##  [79] magrittr_2.0.3              R6_2.5.1                   
##  [81] IRanges_2.28.0              generics_0.1.2             
##  [83] DelayedArray_0.20.0         DBI_1.1.2                  
##  [85] pillar_1.7.0                haven_2.5.0                
##  [87] withr_2.5.0                 survival_3.3-1             
##  [89] RCurl_1.98-1.6              modelr_0.1.8               
##  [91] crayon_1.5.1                utf8_1.2.2                 
##  [93] iZID_0.0.1                  tzdb_0.3.0                 
##  [95] rmarkdown_2.14              grid_4.1.0                 
##  [97] readxl_1.4.0                WeightSVM_1.7-16           
##  [99] reprex_2.0.1                digest_0.6.29              
## [101] numDeriv_2016.8-1.1         mpath_0.4-2.26             
## [103] glmnet_4.1-8                stats4_4.1.0               
## [105] munsell_0.5.0               bslib_0.3.1